Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (5): 1648-1663.doi: 10.13229/j.cnki.jdxbgxb.20230849

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A heuristic task offloading approach with delay and energy constraints for edge-cloud collaboration

Ming-feng SU1,2(),Guo-jun WANG3(),Cong ZHOU1,Tian WANG4   

  1. 1.School of Computer Science，Hunan First Normal University，Changsha 410205，China
    2.School of Computer Science and Engineering，Central South University，Changsha 410083，China
    3.School of Computer Science and Cyber Engineering，Guangzhou University，Guangzhou 510006，China
    4.Institute of Artificial Intelligence and Future Networks，Beijing Normal University，Zhuhai 519087，China
  • Received:2023-08-11 Online:2025-05-01 Published:2025-07-18
  • Contact: Guo-jun WANG E-mail:mfsu@hnfnu.edu.cn;csgjwang@gzhu.edu.cn

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Abstract:

To address the problems of load imbalance， task delay， and increased energy consumption caused by limited device resources and complex task variations in mobile edge computing， a computing task offloading approach with delay and energy constraints for edge-cloud collaboration is proposed， inspired by the cooperative foraging search of sparrow populations. Firstly， adapting to mobile edge cloud collaboration， designing the flyer improved producer update， sine-cosine perturbed follower update， and adaptively adjusted alerter update， a multi-strategy improved sparrow search algorithm （MSSA） is proposed to optimize task offloading location. Then， considering the task maximum completion deadline and delay relaxation variables， incorporating the timeout penalty energy consumption， a heuristic task offloading with MSSA algorithm （HTMA） is proposed， which greedily compares the total task delay and total task energy consumption of pre-offloading location sets under different delay constraints to further optimize task offloading. Experimental results show that compared with similar algorithms， MSSA can improve the optimization accuracy， convergence speed， and robustness of location search. Moreover， HTMA adapts to network changes with better performance of average task completion delay， total task energy consumption， and node load balancing degree.

Key words: edge computing, task offloading, edge-cloud collaboration, heuristic algorithm, cloud computing

CLC Number: 

  • TP301.6

Fig.1

Mobile edge-cloud collaborative computing model"

Fig.2

Producer's task offloading location search space （?rv<?wv）"

Fig.3

Change curve of θτ and ρτ（τmax=500）"

Table 1

Parameters of experimental environment"

参数单位值范围
任务数据量kB［600，1 000］
任务计算量cycle·kbit-1［20，100］
任务最大完成期限ms50
无线传输功率W［2，5］
边缘设备的最大无线链路带宽Mbit/s100
信道增益系数［11］8.2-8~1.5-6
噪声功率W［1，2］?10-8
边缘设备的最大算力GHz2×［2，4］
边缘设备的有线链路带宽Mbit/s1 000
边缘设备的有线传输功率W［1，3］
边缘设备任务执行功率系数W·Hz-3［1.2，1.5］×10-26
云中心任务执行功率W［2.1，2.5］×10-25

Table 2

Setting of algorithm parameters"

算法参数
MSSAμsd=20， ?wv=0.8， μswmax=30， μswmin=3， n（j）=100
SSAμsd=20， ?wv=0.8， μsw=20， n（j）=100
RWSSAμsd=20， ?wv=0.8， μsw=20， n（j）=100
AWPSOC1=1.5， C2=1.5， W=0.8， n（j）=100

Fig.4

Performance of algorithms （n（i）=100， n（s）=5）"

Fig.5

Fitness value of algorithm for different tasks"

Fig.6

Convergence of algorithm for different tasks"

Fig.7

ATCD for different tasks （n（s）=5）"

Fig.8

ATCD for different edge devices （n（i）=100）"

Fig.9

TTEC for different tasks （n（s）=5）"

Fig.10

TTEC for different edge devices （n（i）=100）"

Fig.11

NLBD for different tasks （n（s）=5）"

Fig.12

NLBD for different edge devices （n（i）=100）"

Fig.13

TTCD and TTEC for different γ （n（i）=100， n（s）=5）"

Fig.14

TTCD and TTEC for different tξ （n（i）=100， n（s）=5）"

Fig.15

Overall system service efficiency for different ξ（n（i）=100， n（s）=5）"

Fig.16

TTCD and TTEC for different ωT （n（i）=100， n（s）=5）"

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